ABSTRACT
Accurate determination of intermolecular non-covalent-bonded or non-bonded interactions is the key to potentially useful molecular dynamics simulations of polymer systems. However, it is challenging to balance both the accuracy and computational cost in force field modelling. One of the main difficulties is properly representing the calculated energy data as a continuous force function. In this paper, we employ well-developed machine learning techniques to construct a general purpose intermolecular non-bonded interaction force field for organic polymers. The original ab initio dataset SOFG-31 was calculated by us and has been well documented, and here we use it as our training set. The CLIFF kernel type machine learning scheme is used for predicting the interaction energies of heterodimers selected from the SOFG-31 dataset. Our test results show that the overall errors are well below the chemical accuracy of about 1 kcal/mol, thus demonstrating the promising feasibility of machine learning techniques in force field modelling.
ABSTRACT
A wing specific F1 genetic screen was carried out using the powerful Drosophila genetic system, combined with yeast FRT/FLP and GAL4/UAS system. Form the wing phenotypes and germline clone embryonic cuticle phenotypes observed in these mutant alleles, a number of mutant alleles of known or unknown genes were isolated. Among them, fifteen mutant alleles related to Wingless signal transduction were further isolated; the arm of these mutations located were determined, and their location in the chromosome were roughly mapped.
